In [ ]:
from PIL import Image, ImageEnhance
import numpy as np
import matplotlib.pyplot as plt
import cv2
import os
import pandas as pd
import requests
import json
import re
import csv
directory_path = os.getcwd()
parent_directory_path = os.path.dirname(directory_path)
csv_path = os.path.join(parent_directory_path, 'Model\\condo_data_new_FINAL.csv')
gt_masked_image_path = os.path.join(parent_directory_path, 'Model\\clear\\test')
generated_image_path = os.path.join(parent_directory_path, 'Model\\clear\\final_clear_output_1')
# Read the CSV file
data = pd.read_csv(csv_path)
# Function to extract the numeric part of the filename
def extract_numeric_part(filename):
numeric_part = ''.join(filter(str.isdigit, filename))
return int(numeric_part) if numeric_part else None
def create_binary_mask(arr, target_color, threshold=30):
lower_bound = np.array(target_color) - threshold
upper_bound = np.array(target_color) + threshold
mask = (arr[:, :, :3] >= lower_bound) & (arr[:, :, :3] <= upper_bound)
return np.all(mask, axis=-1)
def extract_building_regions(arr, target_color, threshold=10):
lower_bound = np.array(target_color) - threshold
upper_bound = np.array(target_color) + threshold
mask = (arr[:, :, :3] >= lower_bound) & (arr[:, :, :3] <= upper_bound)
return np.all(mask, axis=-1)
def find_max_building_storeys(gpr):
max_building_storeys= 0
if gpr >= 0 and gpr < 1.4:
max_building_storeys = 5
elif gpr >= 1.4 and gpr < 1.6:
max_building_storeys = 12
elif gpr >= 1.6 and gpr < 2.1:
max_building_storeys = 24
elif gpr >= 2.1 and gpr < 2.8:
max_building_storeys = 36
elif gpr >= 2.8:
max_building_storeys = 48 ## by right got no limit
return max_building_storeys
def masked_rgb(simp_gpr):
rgb = [0,0,0]
if simp_gpr == 1.4:
rgb = [0,255,0]
elif simp_gpr == 1.6:
rgb = [200,130,60]
elif simp_gpr == 2.1:
rgb = [255, 255, 0]
elif simp_gpr == 2.8:
rgb = [255,0,0]
elif simp_gpr == 3.0:
rgb =[0,0,255]
return rgb
'''
pink, [255, 10, 169]
brown, [200,130,60]
cyan, [0,255,255]
red, [255,0,0]
black, [0,0,0]
green, [0,255,0]
blue, [0,0,255]
yellow, [255, 255, 0]
'''
accuracies = []
absolute_accuracies = []
images =[]
sanity_ratio =[]
# Iterate through the images in the generated_image_path
for image_file in os.listdir(generated_image_path):
if image_file.endswith('.png'):
image_index = extract_numeric_part(image_file)
# Construct the path for the corresponding masked image
gt_mask_image_filename = f"{image_index}.png"
gt_mask_image = os.path.join(gt_masked_image_path, gt_mask_image_filename)
open_gt_mask_image = Image.open(gt_mask_image)
mask_crop_box = (512, 0, 1024, 512) # right side
mask_image = open_gt_mask_image.crop(mask_crop_box) #gt_mask is concatenated gt and mask
gt_crop_box = (0, 0, 512, 512) # left side
gt_image = open_gt_mask_image.crop(gt_crop_box)
generated_image = os.path.join(generated_image_path, image_file)
generated_image = Image.open(generated_image)
# Check if the image index matches any index in the CSV
matched_row = data[data['key1'] == image_index]
if not matched_row.empty:
# Extract the GPR value for the matched row
gpr_value = matched_row['GPR'].iloc[0]
simplified_gpr_value = matched_row['simp_gpr'].iloc[0]
actual_site_area = matched_row['area'].iloc[0]
actual_site_area = actual_site_area.replace(',', '')
actual_site_area = float(actual_site_area[:-4])
gpr_value = float(gpr_value)
mask_array = np.array(mask_image)
generated_array = np.array(generated_image)
mask_color = masked_rgb(simplified_gpr_value)
site_mask = create_binary_mask(mask_array, mask_color)
site_area_array = generated_array.copy()
site_area_array[~site_mask] = [255, 255, 255, 255] # making non-masked region white RMB ITS 4 CHANNELS NOW
site_area_image = Image.fromarray(site_area_array)
mask_color = [255, 10, 169] # pink
building_mask = extract_building_regions(site_area_array, mask_color)
buildings_image = Image.fromarray(building_mask)
plt.figure(figsize=(20, 5))
plt.subplot(1, 4, 1)
plt.imshow(mask_image)
plt.title('Mask Image')
plt.axis('off')
plt.subplot(1, 4, 2)
plt.imshow(gt_image)
plt.title('GT Image')
plt.axis('off')
plt.subplot(1, 4, 3)
plt.imshow(generated_image)
plt.title('Generated Image')
plt.axis('off')
plt.subplot(1, 4, 4)
plt.imshow(buildings_image, cmap='gray')
plt.title('Buildings Image')
plt.axis('off')
plt.show()
# accuracy
building_pixels = np.sum(building_mask)
mask_pixels = np.sum(site_mask)
msq_per_pixel = actual_site_area/mask_pixels
building_area = msq_per_pixel * building_pixels
max_storeys = find_max_building_storeys(gpr_value)
generated_gpr = building_area*max_storeys/actual_site_area
accuracy = (gpr_value - generated_gpr) / gpr_value #gpr_value is the target gpr
images.append(image_file)
accuracies.append(accuracy)
absolute_accuracy = abs(accuracy)
absolute_accuracies.append(absolute_accuracy)
print(f'Image: {image_file}, GPR: {gpr_value}, Simplified GPR: {simplified_gpr_value}, Site area: {actual_site_area}, Building pixels: {building_pixels}, Mask pixels: {mask_pixels}, Generated GPR: {generated_gpr}')
#sanity check. ratios should be about 0.75
ratio = mask_pixels/actual_site_area
sanity_ratio.append(ratio)
print('Accuracies: ', accuracies)
print(f"Absolute accuracies: {absolute_accuracies}")
print('Images: ', images)
print(f"Sanity Ratio: {sanity_ratio}")
total_data = len(accuracies)
model_accuracy_1 = sum(accuracies)/total_data
model_accuracy_2 = sum(absolute_accuracies)/total_data
print(f"\nAccuracy for clear data: \naccuracy1: {model_accuracy_1} accuracy2: {model_accuracy_2}")
Image: 1040.png, GPR: 1.4, Simplified GPR: 1.4, Site area: 23065.1, Building pixels: 5099, Mask pixels: 15996, Generated GPR: 3.8252063015753937
Image: 1074.png, GPR: 2.5, Simplified GPR: 2.8, Site area: 37265.0, Building pixels: 3040, Mask pixels: 27225, Generated GPR: 4.019834710743801
Image: 1076.png, GPR: 2.8, Simplified GPR: 2.8, Site area: 10414.2, Building pixels: 732, Mask pixels: 8425, Generated GPR: 4.170445103857566
Image: 1102.png, GPR: 1.6, Simplified GPR: 1.6, Site area: 6157.3, Building pixels: 1596, Mask pixels: 4766, Generated GPR: 8.036928241712129
Image: 1180.png, GPR: 3.0, Simplified GPR: 3.0, Site area: 19547.0, Building pixels: 3950, Mask pixels: 14134, Generated GPR: 13.414461582000849
Image: 1379.png, GPR: 1.4, Simplified GPR: 1.4, Site area: 17455.9, Building pixels: 4904, Mask pixels: 12042, Generated GPR: 4.88689586447434
Image: 145.png, GPR: 2.8, Simplified GPR: 2.8, Site area: 22094.4, Building pixels: 2447, Mask pixels: 16092, Generated GPR: 7.29903057419836
Image: 1484.png, GPR: 3.0, Simplified GPR: 3.0, Site area: 10097.1, Building pixels: 2483, Mask pixels: 7503, Generated GPR: 15.88484606157537
Image: 1602.png, GPR: 3.0, Simplified GPR: 3.0, Site area: 13564.8, Building pixels: 3324, Mask pixels: 9811, Generated GPR: 16.262562429925595
Image: 1655.png, GPR: 2.1, Simplified GPR: 2.1, Site area: 27418.2, Building pixels: 5555, Mask pixels: 21801, Generated GPR: 9.172973716801982
Image: 1670.png, GPR: 2.8, Simplified GPR: 2.8, Site area: 17940.2, Building pixels: 1689, Mask pixels: 11661, Generated GPR: 6.952405454077694
Image: 1796.png, GPR: 2.8, Simplified GPR: 2.8, Site area: 13877.2, Building pixels: 1266, Mask pixels: 9220, Generated GPR: 6.5908893709327545
Image: 1811.png, GPR: 1.4, Simplified GPR: 1.4, Site area: 7255.7, Building pixels: 2614, Mask pixels: 5084, Generated GPR: 6.169944925255704
Image: 1876.png, GPR: 2.1, Simplified GPR: 2.1, Site area: 10502.8, Building pixels: 2576, Mask pixels: 8279, Generated GPR: 11.201352820388937
Image: 191.png, GPR: 3.5, Simplified GPR: 3.0, Site area: 13000.3, Building pixels: 2704, Mask pixels: 9066, Generated GPR: 14.316346790205161
Image: 2000.png, GPR: 3.0, Simplified GPR: 3.0, Site area: 13241.8, Building pixels: 3157, Mask pixels: 9503, Generated GPR: 15.946122277175633
Image: 434.png, GPR: 2.1, Simplified GPR: 2.1, Site area: 39401.6, Building pixels: 5920, Mask pixels: 28712, Generated GPR: 7.422680412371133
Image: 489.png, GPR: 2.1, Simplified GPR: 2.1, Site area: 28692.65, Building pixels: 4316, Mask pixels: 20518, Generated GPR: 7.572667901354908
Image: 491.png, GPR: 3.0, Simplified GPR: 3.0, Site area: 18747.8, Building pixels: 3560, Mask pixels: 12878, Generated GPR: 13.269141170989284
Image: 568.png, GPR: 3.4, Simplified GPR: 3.0, Site area: 14344.0, Building pixels: 3006, Mask pixels: 10352, Generated GPR: 13.93817619783617
Image: 859.png, GPR: 3.5, Simplified GPR: 3.0, Site area: 25272.5, Building pixels: 4992, Mask pixels: 20268, Generated GPR: 11.822380106571936 Accuracies: [-1.7322902154109956, -0.6079338842975204, -0.4894446799491308, -4.02308015107008, -3.4714871940002827, -2.4906399031959574, -1.6067966336422717, -4.29494868719179, -4.4208541433085315, -3.3680827222866583, -1.483001947884891, -1.3538890610474126, -3.4071035180397886, -4.333977533518541, -3.0903847972014744, -4.3153740923918775, -2.53460972017673, -2.606032333978528, -3.423047056996428, -3.099463587598873, -2.377822887591982] Absolute accuracies: [1.7322902154109956, 0.6079338842975204, 0.4894446799491308, 4.02308015107008, 3.4714871940002827, 2.4906399031959574, 1.6067966336422717, 4.29494868719179, 4.4208541433085315, 3.3680827222866583, 1.483001947884891, 1.3538890610474126, 3.4071035180397886, 4.333977533518541, 3.0903847972014744, 4.3153740923918775, 2.53460972017673, 2.606032333978528, 3.423047056996428, 3.099463587598873, 2.377822887591982] Images: ['1040.png', '1074.png', '1076.png', '1102.png', '1180.png', '1379.png', '145.png', '1484.png', '1602.png', '1655.png', '1670.png', '1796.png', '1811.png', '1876.png', '191.png', '2000.png', '434.png', '489.png', '491.png', '568.png', '859.png'] Sanity Ratio: [0.693515311011008, 0.7305782906212264, 0.8089915692035874, 0.7740405697302388, 0.7230777101345475, 0.6898527145549642, 0.7283293504236367, 0.7430846480672668, 0.7232690493040812, 0.7951287830710987, 0.649992753703972, 0.6643991583316519, 0.7006904916134901, 0.7882659862132003, 0.6973685222648709, 0.717651678774789, 0.7287013725330951, 0.7150960263342703, 0.6869072637856175, 0.7216954824316788, 0.8019784350578693] Accuracy for clear data: accuracy1: -2.7871554643228453 accuracy2: 2.7871554643228453